As the development of software applications evolves, programming accurate and efficient code has become a real challenge. A typical software application interfaces with different modules, e.g., using APIs, and should be programmed to enable its execution on different platforms and operating systems. For example, a software application (app) should be available for execution over different types of operating systems such as Linux®, Mac OS® and Windows®. Furthermore, as execution of many software applications has been migrated to cloud computing infrastructures, the code should be written such that its execution is supported by any computing environments in the cloud infrastructure. Thus, in sum, the complexity of software applications imposes a real challenge on programmers.
In order to enable the rapid creation, setup, and deployment of applications, resources that include “off-the-shelf” functions, algorithms, and pieces of codes are available. Such resources range from academic and scientific publications to code repositories (e.g., GitHub). However, such resources are limited in that they cannot provide executable code that can be easily evaluated and integrated in a software application or project being researched or developed. For example, a scientific publication (e.g., in an IEEE® article) would likely include a textual description of an algorithm or at best its pseudo code rather than the code itself. Further, even if such a resource provides executable code, such code may not be compatible with the software application or project as is.
Learning and evaluating code-focused scientific publications and text books (collectively referred to as “printed publications”) can be difficult and limiting. That is, printed publications generally do not include any code that can be immediately executed on a computing environment that the software application is developed for. At best, printed publications include a detailed description of the code, some samples, and/or pseudo code. The reader has no way to “experience” the code and/or to otherwise judge whether the code described in the printed publications would serve its purpose in a computer program once deployed in a computing environment.
Thus, to evaluate the functionality and performance of the algorithm, executable code should be coded and tested in that computing environment. This is a time consuming and error prone task. It should be emphasized that drawbacks of printed publications as discussed herein are equally relevant when the printed publications are electronic versions. That is, there is no way to readily evaluate the functionality and performance of the algorithm or code, as described in either a printed or an electronic version, in a computing environment.
Further, publishers of printed and/or electronic publications suffer from loss of revenue as researches increasingly turn to search engines for information. A major concern for publishers is that printed publications (and particularly textbooks) will become obsolete. This is mainly due to the fact that text books and publications currently do not offer any mechanisms that can be helpful to programmers or researches when developing algorithms.
Furthermore, due to the voluminous amount of academic and scientific publications, open-source codes, and other algorithm resources, it is almost impossible to search for the right algorithm that would fit into a software application and would perform an intended function. Additionally, it can be difficult to organize additional references or information obtained from sources other than a particular printed or electronic publication.
It would therefore be advantageous to provide a solution that overcomes the deficiencies noted above by enabling integration of algorithms' codes into printed publications.